






import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE = None     ,     )     ->    Tuple:
							_lowercase							:   List[str] 							=   {}

							if train_file is not None:
														_lowercase							:   Tuple 							=   [train_file]
							if eval_file is not None:
														_lowercase							:   Optional[Any] 							=   [eval_file]
							if test_file is not None:
														_lowercase							:   Dict 							=   [test_file]

							_lowercase							:   Dict 							=   datasets.load_dataset('csv'     ,     data_files=SCREAMING_SNAKE_CASE				)
							_lowercase							:   Dict 							=   list(ds[list(files.keys()				)[0]].features.keys()				)
							_lowercase							:   List[Any] 							=   features_name.pop(SCREAMING_SNAKE_CASE				)
							_lowercase							:   Any 							=   list(set(ds[list(files.keys()				)[0]][label_name]				)				)
							_lowercase							:   Dict 							=   {label: i for i, label in enumerate(SCREAMING_SNAKE_CASE				)}
							_lowercase							:   Tuple 							=   tokenizer.model_input_names
							_lowercase							:   int 							=   {}

							if len(SCREAMING_SNAKE_CASE				) == 1:
														for k in files.keys():
																					_lowercase							:   List[str] 							=   ds[k].map(
																					    lambda SCREAMING_SNAKE_CASE				: tokenizer.batch_encode_plus(
																					        example[features_name[0]]     ,     truncation=SCREAMING_SNAKE_CASE     ,     max_length=SCREAMING_SNAKE_CASE     ,     padding='max_length'				)     ,     batched=SCREAMING_SNAKE_CASE     ,     )
							elif len(SCREAMING_SNAKE_CASE				) == 2:
														for k in files.keys():
																					_lowercase							:   Tuple 							=   ds[k].map(
																					    lambda SCREAMING_SNAKE_CASE				: tokenizer.batch_encode_plus(
																					        (example[features_name[0]], example[features_name[1]])     ,     truncation=SCREAMING_SNAKE_CASE     ,     max_length=SCREAMING_SNAKE_CASE     ,     padding='max_length'     ,     )     ,     batched=SCREAMING_SNAKE_CASE     ,     )

							def gen_train():
														for ex in transformed_ds[datasets.Split.TRAIN]:
																					_lowercase							:   str 							=   {k: v for k, v in ex.items() if k in input_names}
																					_lowercase							:   List[str] 							=   labelaid[ex[label_name]]
																					yield (d, label)

							def gen_val():
														for ex in transformed_ds[datasets.Split.VALIDATION]:
																					_lowercase							:   str 							=   {k: v for k, v in ex.items() if k in input_names}
																					_lowercase							:   Tuple 							=   labelaid[ex[label_name]]
																					yield (d, label)

							def gen_test():
														for ex in transformed_ds[datasets.Split.TEST]:
																					_lowercase							:   int 							=   {k: v for k, v in ex.items() if k in input_names}
																					_lowercase							:   str 							=   labelaid[ex[label_name]]
																					yield (d, label)

							_lowercase							:   str 							=   (
							    tf.data.Dataset.from_generator(
							        SCREAMING_SNAKE_CASE     ,     ({k: tf.intaa for k in input_names}, tf.intaa)     ,     ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))     ,     )
							    if datasets.Split.TRAIN in transformed_ds
							    else None
							)

							if train_ds is not None:
														_lowercase							:   Optional[int] 							=   train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]				)				)				)

							_lowercase							:   int 							=   (
							    tf.data.Dataset.from_generator(
							        SCREAMING_SNAKE_CASE     ,     ({k: tf.intaa for k in input_names}, tf.intaa)     ,     ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))     ,     )
							    if datasets.Split.VALIDATION in transformed_ds
							    else None
							)

							if val_ds is not None:
														_lowercase							:   str 							=   val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]				)				)				)

							_lowercase							:   List[str] 							=   (
							    tf.data.Dataset.from_generator(
							        SCREAMING_SNAKE_CASE     ,     ({k: tf.intaa for k in input_names}, tf.intaa)     ,     ({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))     ,     )
							    if datasets.Split.TEST in transformed_ds
							    else None
							)

							if test_ds is not None:
														_lowercase							:   Optional[Any] 							=   test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]				)				)				)

							return train_ds, val_ds, test_ds, labelaid


UpperCamelCase								=		logging.getLogger(__name__)

@dataclass
class 				lowerCAmelCase_	:
				_UpperCamelCase					:       int							       = field(metadata={"help": "Which column contains the label"}			)
				_UpperCamelCase					:       str							       = field(default=__snake_case   ,		metadata={"help": "The path of the training file"}			)
				_UpperCamelCase					:       Optional[str]							       = field(default=__snake_case   ,		metadata={"help": "The path of the development file"}			)
				_UpperCamelCase					:       Optional[str]							       = field(default=__snake_case   ,		metadata={"help": "The path of the test file"}			)
				_UpperCamelCase					:       int							       = field(
				    default=128   ,		metadata={
				        "help": (
				            "The maximum total input sequence length after tokenization. Sequences longer "
				            "than this will be truncated, sequences shorter will be padded."
				        )
				    }   ,		)
				_UpperCamelCase					:       bool							       = field(
				    default=__snake_case   ,		metadata={"help": "Overwrite the cached training and evaluation sets"}			)




@dataclass
class 				lowerCAmelCase_	:
				_UpperCamelCase					:       str							       = field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			)
				_UpperCamelCase					:       Optional[str]							       = field(
				    default=__snake_case   ,		metadata={"help": "Pretrained config name or path if not the same as model_name"}			)
				_UpperCamelCase					:       Optional[str]							       = field(
				    default=__snake_case   ,		metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}			)
				_UpperCamelCase					:       bool							       = field(default=__snake_case   ,		metadata={"help": "Set this flag to use fast tokenization."}			)
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				_UpperCamelCase					:       Optional[str]							       = field(
				    default=__snake_case   ,		metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,		)






def    __magic_name__    (	)     ->    str:
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.
							_lowercase							:   Optional[int] 							=   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
							_lowercase							,							_lowercase							,							_lowercase							:   Optional[int] 							=   parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir				)
							    and os.listdir(training_args.output_dir				)
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
														    ' --overwrite_output_dir to overcome.'				)

							# Setup logging
							logging.basicConfig(
							    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'     ,     datefmt='%m/%d/%Y %H:%M:%S'     ,     level=logging.INFO     ,     )
							logger.info(
							    F"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1				)}, """
							    F"""16-bits training: {training_args.fpaa}"""				)
							logger.info(F"""Training/evaluation parameters {training_args}"""				)

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							_lowercase							:   List[str] 							=   AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     ,     cache_dir=model_args.cache_dir     ,     )

							_lowercase							,							_lowercase							,							_lowercase							,							_lowercase							:   int 							=   get_tfds(
							    train_file=data_args.train_file     ,     eval_file=data_args.dev_file     ,     test_file=data_args.test_file     ,     tokenizer=SCREAMING_SNAKE_CASE     ,     label_column_id=data_args.label_column_id     ,     max_seq_length=data_args.max_seq_length     ,     )

							_lowercase							:   List[Any] 							=   AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,     num_labels=len(SCREAMING_SNAKE_CASE				)     ,     labelaid=SCREAMING_SNAKE_CASE     ,     idalabel={id: label for label, id in labelaid.items()}     ,     finetuning_task='text-classification'     ,     cache_dir=model_args.cache_dir     ,     )

							with training_args.strategy.scope():
														_lowercase							:   List[str] 							=   TFAutoModelForSequenceClassification.from_pretrained(
														    model_args.model_name_or_path     ,     from_pt=bool('.bin' in model_args.model_name_or_path				)     ,     config=SCREAMING_SNAKE_CASE     ,     cache_dir=model_args.cache_dir     ,     )

							def compute_metrics(SCREAMING_SNAKE_CASE				) -> Dict:
														_lowercase							:   Optional[int] 							=   np.argmax(p.predictions     ,     axis=1				)

														return {"acc": (preds == p.label_ids).mean()}

							# Initialize our Trainer
							_lowercase							:   Optional[Any] 							=   TFTrainer(
							    model=SCREAMING_SNAKE_CASE     ,     args=SCREAMING_SNAKE_CASE     ,     train_dataset=SCREAMING_SNAKE_CASE     ,     eval_dataset=SCREAMING_SNAKE_CASE     ,     compute_metrics=SCREAMING_SNAKE_CASE     ,     )

							# Training
							if training_args.do_train:
														trainer.train()
														trainer.save_model()
														tokenizer.save_pretrained(training_args.output_dir				)

							# Evaluation
							_lowercase							:   Dict 							=   {}
							if training_args.do_eval:
														logger.info('*** Evaluate ***'				)
														_lowercase							:   int 							=   trainer.evaluate()
														_lowercase							:   List[Any] 							=   os.path.join(training_args.output_dir     ,     'eval_results.txt'				)

														with open(SCREAMING_SNAKE_CASE     ,     'w'				) as writer:
																					logger.info('***** Eval results *****'				)

																					for key, value in result.items():
																												logger.info(F"""  {key} = {value}"""				)
																												writer.write(F"""{key} = {value}\n"""				)

																					results.update(SCREAMING_SNAKE_CASE				)

							return results


if __name__ == "__main__":
							main()










'''simple docstring'''







import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()

class 	_snake_case(			UpperCAmelCase       , UpperCAmelCase       , UpperCAmelCase       , unittest.TestCase      ):
 __snake_case:							Union[str, Any]						=					StableUnCLIPPipeline
 __snake_case:							List[str]						=					TEXT_TO_IMAGE_PARAMS
 __snake_case:							str						=					TEXT_TO_IMAGE_BATCH_PARAMS
 __snake_case:							Dict						=					TEXT_TO_IMAGE_IMAGE_PARAMS
 __snake_case:							List[str]						=					TEXT_TO_IMAGE_IMAGE_PARAMS

 # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
 __snake_case:							str						=					False






 def 						_UpperCamelCase (self       :  Optional[int]    )       ->   int:






    """simple docstring"""


    A__       =     32
    A__       =     embedder_hidden_size

    # prior components

    torch.manual_seed(0    )
    A__       =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

    torch.manual_seed(0    )
    A__       =     CLIPTextModelWithProjection(
        CLIPTextConfig(
            bos_token_id=0    ,		eos_token_id=2    ,		hidden_size=a    ,		projection_dim=a    ,		intermediate_size=37    ,		layer_norm_eps=1e-05    ,		num_attention_heads=4    ,		num_hidden_layers=5    ,		pad_token_id=1    ,		vocab_size=10_00    ,		)    )

    torch.manual_seed(0    )
    A__       =     PriorTransformer(
        num_attention_heads=2    ,		attention_head_dim=12    ,		embedding_dim=a    ,		num_layers=1    ,		)

    torch.manual_seed(0    )
    A__       =     DDPMScheduler(
        variance_type='fixed_small_log'    ,		prediction_type='sample'    ,		num_train_timesteps=10_00    ,		clip_sample=a    ,		clip_sample_range=5.0    ,		beta_schedule='squaredcos_cap_v2'    ,		)

    # regular denoising components

    torch.manual_seed(0    )
    A__       =     StableUnCLIPImageNormalizer(embedding_dim=a    )
    A__       =     DDPMScheduler(beta_schedule='squaredcos_cap_v2'    )

    torch.manual_seed(0    )
    A__       =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

    torch.manual_seed(0    )
    A__       =     CLIPTextModel(
        CLIPTextConfig(
            bos_token_id=0    ,		eos_token_id=2    ,		hidden_size=a    ,		projection_dim=32    ,		intermediate_size=37    ,		layer_norm_eps=1e-05    ,		num_attention_heads=4    ,		num_hidden_layers=5    ,		pad_token_id=1    ,		vocab_size=10_00    ,		)    )

    torch.manual_seed(0    )
    A__       =     UNetaDConditionModel(
        sample_size=32    ,		in_channels=4    ,		out_channels=4    ,		down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')    ,		up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')    ,		block_out_channels=(32, 64)    ,		attention_head_dim=(2, 4)    ,		class_embed_type='projection'    ,		projection_class_embeddings_input_dim=embedder_projection_dim * 2    ,		cross_attention_dim=a    ,		layers_per_block=1    ,		upcast_attention=a    ,		use_linear_projection=a    ,		)

    torch.manual_seed(0    )
    A__       =     DDIMScheduler(
        beta_schedule='scaled_linear'    ,		beta_start=0.0_0085    ,		beta_end=0.012    ,		prediction_type='v_prediction'    ,		set_alpha_to_one=a    ,		steps_offset=1    ,		)

    torch.manual_seed(0    )
    A__       =     AutoencoderKL()

    A__       =     {
        # prior components
        'prior_tokenizer': prior_tokenizer,
        'prior_text_encoder': prior_text_encoder,
        'prior': prior,
        'prior_scheduler': prior_scheduler,
        # image noising components
        'image_normalizer': image_normalizer,
        'image_noising_scheduler': image_noising_scheduler,
        # regular denoising components
        'tokenizer': tokenizer,
        'text_encoder': text_encoder,
        'unet': unet,
        'scheduler': scheduler,
        'vae': vae,
    }

    return components






 def 						_UpperCamelCase (self       :  List[Any]    ,		a       :  Optional[Any]    ,		a       :  str=0    )       ->   Dict:






    """simple docstring"""


    if str(a    ).startswith('mps'    ):
       A__       =     torch.manual_seed(a    )
    else:
       A__       =     torch.Generator(device=a    ).manual_seed(a    )
    A__       =     {
        'prompt': 'A painting of a squirrel eating a burger',
        'generator': generator,
        'num_inference_steps': 2,
        'prior_num_inference_steps': 2,
        'output_type': 'numpy',
    }
    return inputs






 def 						_UpperCamelCase (self       :  List[str]    )       ->   Optional[int]:






    """simple docstring"""


    A__       =     torch_device == 'cpu'

    self._test_attention_slicing_forward_pass(test_max_difference=a    )






 def 						_UpperCamelCase (self       :  Dict    )       ->   str:






    """simple docstring"""


    A__       =     torch_device in ['cpu', 'mps']

    self._test_inference_batch_single_identical(test_max_difference=a    )




@slow
@require_torch_gpu
class 	_snake_case(			unittest.TestCase      ):






 def 						_UpperCamelCase (self       :  Any    )       ->   Optional[Any]:






    """simple docstring"""


    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()






 def 						_UpperCamelCase (self       :  Dict    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'    )

    A__       =     StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'    ,		torch_dtype=torch.floataa    )
    pipe.to(a    )
    pipe.set_progress_bar_config(disable=a    )
    # stable unclip will oom when integration tests are run on a V100,
    # so turn on memory savings
    pipe.enable_attention_slicing()
    pipe.enable_sequential_cpu_offload()

    A__       =     torch.Generator(device='cpu'    ).manual_seed(0    )
    A__       =     pipe('anime turle'    ,		generator=a    ,		output_type='np'    )

    A__       =     output.images[0]

    assert image.shape == (7_68, 7_68, 3)

    assert_mean_pixel_difference(a    ,		a    )






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   str:






    """simple docstring"""


    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()

    A__       =     StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'    ,		torch_dtype=torch.floataa    )
    A__       =     pipe.to(a    )
    pipe.set_progress_bar_config(disable=a    )
    pipe.enable_attention_slicing()
    pipe.enable_sequential_cpu_offload()

    A__       =     pipe(
        'anime turtle'    ,		prior_num_inference_steps=2    ,		num_inference_steps=2    ,		output_type='np'    ,		)

    A__       =     torch.cuda.max_memory_allocated()
    # make sure that less than 7 GB is allocated
    assert mem_bytes < 7 * 10**9


